
import numpy as np
import pandas as pd
import os,sys
from pathlib import Path
import matplotlib.pyplot as plt

cwd = os.getcwd() #grabs current working directory

testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP' / 'EnergyAnalysis')
inputfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP')
baselinesfolder = str(Path().resolve().parent.parent /'PV_ICE' / 'baselines')
supportMatfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'baselines' / 'SupportingMaterial')

if not os.path.exists(testfolder):
    os.makedirs(testfolder)


#https://www.learnui.design/tools/data-color-picker.html#palette
#color pallette - modify here for all graphs below
colorpalette=['#003f5c','#374c80','#7a5195','#bc5090','#ef5675','#ff764a','#ffa600']
import matplotlib as mpl #import matplotlib
from cycler import cycler #import cycler
mpl.rcParams['axes.prop_cycle'] = cycler(color=colorpalette) #reset the default color palette of mpl

plt.rcParams.update({'font.size': 14})
plt.rcParams['figure.figsize'] = (8, 6)

scennames_labels = ['PV_ICE','PERC_50','High Eff','Idealized\nPerovskite\nSi-Tandem','Lightweight'
                    'SHJ','TOPCon','Recycled\nPERC','Cheap\nCrap'] #


#creating scenarios for identical power of multiple technologies
scennames = ['PV_ICE','PERC_50','High_eff', 'Perovskite','Lightweight', #extreme cases
            'SHJ', 'TOPCon','RecycledPERC','CheapCrap'] #realistic cases
MATERIALS = ['glass','silver','silicon', 'copper', 'aluminium_frames','encapsulant', 'backsheet']
moduleFile_m = os.path.join(baselinesfolder, 'baseline_modules_mass_US.csv')
moduleFile_e = os.path.join(baselinesfolder, 'baseline_modules_energy.csv')


#load in a baseline and materials for modification
import PV_ICE

sim1 = PV_ICE.Simulation(name='sim1', path=testfolder)
for scen in scennames:
    sim1.createScenario(name=scen, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim1.scenario[scen].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)

path = C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\TEMP\EnergyAnalysis


sim1.scenario.keys()

dict_keys(['PV_ICE', 'PERC_50', 'High_eff', 'Perovskite', 'Lightweight', 'SHJ', 'TOPCon', 'RecycledPERC', 'CheapCrap'])


celltech_modeff = pd.read_csv(os.path.join(supportMatfolder, 'output-celltech-modeffimprovements.csv'),index_col=0) #pull in module eff
celltech_aguse = pd.read_csv(os.path.join(supportMatfolder, 'output-celltech-Agusageimprovements.csv'),index_col=0) #pull in Ag use


celltech_modeff.loc[2020]

PERC      22.8
SHJ       23.5
TOPCon    23.2
Name: 2020, dtype: float64


#glass-glass package mass per area calculation
#ITRPV 2022 Figs 36 and 38, we are assuming that the front and back glass heave equal thickness of 2.5mm
density_glass = 2500*1000 # g/m^3 
glassperm2 = (2.5/1000)* 2 * density_glass
print('The mass per module area of glass is '+str(glassperm2)+' g/m^2 for all modules with a glass-glass package')

The mass per module area of glass is 12500.0 g/m^2 for all modules with a glass-glass package


timeshift = 2022-1995


#module efficiency modify for PERC
sim1.modifyScenario('PERC_50', 'mod_eff', celltech_modeff.loc[2022:,'PERC'], start_year=2022) #changing module eff


#silver modify for PERC
sim1.scenario['PERC_50'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'PERC'], start_year=2022)
#old way
#sim1.scenario['PERC_50'].material['silver'].matdataIn_m.loc[timeshift:,'mat_massperm2'] = celltech_aguse.loc[2022:,'PERC'].values


#modify package to glass glass
sim1.scenario['PERC_50'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022) #


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('PERC_50', 'mod_degradation', 0.445, start_year=2022) #annual power degradation to reach 80% at 50 yrs
#T50
sim1.modifyScenario('PERC_50', 'mod_reliability_t50', 56.07, start_year=2022)
#t90
sim1.modifyScenario('PERC_50', 'mod_reliability_t90', 59.15, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('PERC_50', 'mod_lifetime', 50, start_year=2022) #project lifetime of 50 years


#Merchant Tail set high
sim1.modifyScenario('PERC_50', 'mod_MerchantTail', 100, start_year=2022) #all installations stay for merchant tail
#Change recycling?


idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_higheff = pd.DataFrame(index=idx_temp, columns=['mod_eff_p'], dtype=float)
df_higheff.loc[2022] = 23.5
df_higheff.loc[2030] = 26.0
df_higheff.loc[2050] = 32.0
df_higheff.interpolate(inplace=True)
#module efficiency modify for PERC
sim1.modifyScenario('High_eff', 'mod_eff', df_higheff.loc[2022:,], start_year=2022) #changing module eff


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('High_eff', 'mod_degradation', 0.7, start_year=2022) #standard current degrdation
#T50
sim1.modifyScenario('High_eff', 'mod_reliability_t50', 25, start_year=2022)
#t90
sim1.modifyScenario('High_eff', 'mod_reliability_t90', 31, start_year=2022) 
#Mod Project Lifetime
sim1.modifyScenario('High_eff', 'mod_lifetime', 25, start_year=2022) #project lifetime of 25 years


#2022 module eff = 17.9% #https://www.nrel.gov/pv/assets/pdfs/champion-module-efficiencies-rev220401b.pdf
#2050 module eff = 32.5% # https://www.nrel.gov/pv/assets/pdfs/best-research-cell-efficiencies.pdf
idx_perovskite_eff = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_perovskite_eff = pd.DataFrame(index=idx_perovskite_eff, columns=['mod_eff_p'], dtype=float)
df_perovskite_eff.loc[2022] = 17.9
df_perovskite_eff.loc[2050] = 32.5
df_perovskite_eff.interpolate(inplace=True)


#module efficiency modify for PERC
sim1.modifyScenario('Perovskite', 'mod_eff', df_perovskite_eff['mod_eff_p'], start_year=2022) #changing module eff


#modify package to glass glass
sim1.scenario['Perovskite'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('Perovskite', 'mod_degradation', 1.47, start_year=2022) #annual power degradation
#Mod Project Lifetime
sim1.modifyScenario('Perovskite', 'mod_lifetime', 15, start_year=2022) #project lifetime of 15 years
#T50
sim1.modifyScenario('Perovskite', 'mod_reliability_t50', 14, start_year=2022)
#t90
sim1.modifyScenario('Perovskite', 'mod_reliability_t90', 20, start_year=2022)


#As Circular as possible
#100% collection rate
sim1.modifyScenario('Perovskite', 'mod_EOL_collection_eff', 100.0, start_year=2022) #100% collection
sim1.modifyScenario('Perovskite', 'mod_EOL_pg1_landfill', 0.0, start_year=2022) #100% collection
sim1.modifyScenario('Perovskite', 'mod_EOL_pb1_landfill', 0.0, start_year=2022) #100% collection

# remanufacturing
sim1.modifyScenario('Perovskite', 'mod_EOL_pg3_reMFG', 100.0, start_year=2022) #all modules attempt remfg
sim1.modifyScenario('Perovskite', 'mod_EOL_sp_reMFG_recycle', 100.0, start_year=2022) # recycle if can't remfg
sim1.modifyScenario('Perovskite', 'mod_EOL_pb3_reMFG', 100.0, start_year=2022) # remfg bad mods too
sim1.modifyScenario('Perovskite', 'mod_EOL_reMFG_yield', 98.0, start_year=2022) # REMFG YIELD 98%

#set all other paths to 0
sim1.modifyScenario('Perovskite', 'mod_EOL_pg0_resell', 0.0, start_year=2022) # 
sim1.modifyScenario('Perovskite', 'mod_EOL_pg1_landfill', 0.0, start_year=2022) # 
sim1.modifyScenario('Perovskite', 'mod_EOL_pg2_stored', 0.0, start_year=2022) #
sim1.modifyScenario('Perovskite', 'mod_EOL_pg4_recycled', 0.0, start_year=2022) # 
sim1.modifyScenario('Perovskite', 'mod_EOL_pb1_landfill', 0.0, start_year=2022) # 
sim1.modifyScenario('Perovskite', 'mod_EOL_pb2_stored', 0.0, start_year=2022) # 
sim1.modifyScenario('Perovskite', 'mod_EOL_pb4_recycled', 0.0, start_year=2022) # 
sim1.modifyScenario('Perovskite', 'mod_Repair', 0.0, start_year=2022) #
sim1.modifyScenario('Perovskite', 'mod_MerchantTail', 0.0, start_year=2022) #


#Material Remanufacture
#Glass
#mfg scrap
sim1.scenario['Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycling_eff', 99.0, start_year=2022) #99% yield
sim1.scenario['Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['Perovskite'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['Perovskite'].modifyMaterials('glass', 'mat_PG3_ReMFG_target', 100.0, start_year=2022) #send all to remfg
sim1.scenario['Perovskite'].modifyMaterials('glass', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send none to recycle
sim1.scenario['Perovskite'].modifyMaterials('glass', 'mat_ReMFG_yield', 99.0, start_year=2022) #99% yeild
sim1.scenario['Perovskite'].modifyMaterials('glass', 'mat_Recycling_yield', 99.0, start_year=2022) #99% yeild


#silicon Recycle (or should it be remfg?)
#mfg scrap
sim1.scenario['Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['Perovskite'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['Perovskite'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['Perovskite'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['Perovskite'].modifyMaterials('silicon', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['Perovskite'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['Perovskite'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
sim1.scenario['Perovskite'].modifyMaterials('silicon', 'mat_ReMFG_yield', 0.0, start_year=2022) #99% yeild

#aluminium_frames recycle
#mfg scrap
sim1.scenario['Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycling_eff', 99.0, start_year=2022) #98% yield
sim1.scenario['Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['Perovskite'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['Perovskite'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['Perovskite'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 99.0, start_year=2022) #99% yeild
sim1.scenario['Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['Perovskite'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
sim1.scenario['Perovskite'].modifyMaterials('aluminium_frames', 'mat_ReMFG_yield', 0.0, start_year=2022) #0% yeild


#module efficiency modify for PERC
sim1.modifyScenario('Lightweight', 'mod_eff', celltech_modeff.loc[2022:,'PERC'], start_year=2022) #changing module eff


#2mm glass calculation
lightweightglass = (2.0/1000)* 1 * density_glass

#50% encapsulant
light_encap = pd.Series(sim1.scenario['PV_ICE'].material['encapsulant'].matdataIn_m.loc[timeshift:,'mat_massperm2']*0.5)

#10% silicon
light_Si = pd.Series(sim1.scenario['PV_ICE'].material['silicon'].matdataIn_m.loc[timeshift:,'mat_massperm2']*0.1)

#modify package
sim1.scenario['Lightweight'].modifyMaterials('glass', 'mat_massperm2', lightweightglass, start_year=2022) #glass
sim1.scenario['Lightweight'].modifyMaterials('aluminium_frames', 'mat_massperm2', 0.0, start_year=2022) #frameless
sim1.scenario['Lightweight'].modifyMaterials('encapsulant', 'mat_massperm2', light_encap, start_year=2022) #50% encapsulant
sim1.scenario['Lightweight'].modifyMaterials('silicon', 'mat_massperm2', light_Si, start_year=2022) #10% silicon


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('Lightweight', 'mod_degradation', 0.7, start_year=2022) #annual power degradation
#Mod Project Lifetime
sim1.modifyScenario('Lightweight', 'mod_lifetime', 25, start_year=2022) #project lifetime of 15 years
#T50
sim1.modifyScenario('Lightweight', 'mod_reliability_t50', 25, start_year=2022)
#t90
sim1.modifyScenario('Lightweight', 'mod_reliability_t90', 31, start_year=2022)


#module efficiency modify for SHJ
sim1.modifyScenario('SHJ', 'mod_eff', celltech_modeff.loc[2022:,'SHJ'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#modify silver usage for SHJ
sim1.scenario['SHJ'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'SHJ'], start_year=2022)


#modify package to glass glass
sim1.scenario['SHJ'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#Lifetime and Degradation
#median annual power degradation Jordan et al 2022, Table 6
idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_shj_deg = pd.DataFrame(index=idx_temp, columns=['mod_deg'], dtype=float)
df_shj_deg.loc[2022] = 0.7
df_shj_deg.loc[2030] = 0.6
df_shj_deg.loc[2050] = 0.5
df_shj_deg.interpolate(inplace=True)

#degradation rate:
sim1.modifyScenario('SHJ', 'mod_degradation', df_shj_deg.loc[2022:,'mod_deg'], start_year=2022) 

#Mod Project Lifetime
df_shj_life = pd.DataFrame(index=idx_temp, columns=['mod_lifetime'], dtype=float)
df_shj_life.loc[2022] = 30
df_shj_life.loc[2030] = 35
df_shj_life.loc[2050] = 38
df_shj_life.interpolate(inplace=True)
sim1.modifyScenario('SHJ', 'mod_lifetime', df_shj_life.loc[2022:,'mod_lifetime'], start_year=2022) #

#T50
df_shj_t50 = pd.DataFrame(index=idx_temp, columns=['mod_t50'], dtype=float)
df_shj_t50.loc[2022] = 28
df_shj_t50.loc[2030] = 40
df_shj_t50.loc[2050] = 41
df_shj_t50.interpolate(inplace=True)
sim1.modifyScenario('SHJ', 'mod_reliability_t50', df_shj_t50.loc[2022:,'mod_t50'], start_year=2022)
#t90
df_shj_t90 = pd.DataFrame(index=idx_temp, columns=['mod_t90'], dtype=float)
df_shj_t90.loc[2022] = 33
df_shj_t90.loc[2030] = 44
df_shj_t90.loc[2050] = 45
df_shj_t90.interpolate(inplace=True)
sim1.modifyScenario('SHJ', 'mod_reliability_t90', df_shj_t90.loc[2022:,'mod_t90'], start_year=2022)


#Merchant Tail set high
#sim1.modifyScenario('SHJ', 'mod_MerchantTail', 50, start_year=2022) #50% stay for merchant tail, possibly change to DYNAMIC


idx_temp = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
#module collection
df_shj_modcollect = pd.DataFrame(index=idx_temp, columns=['mod_collect'], dtype=float)
df_shj_modcollect.loc[2022] = 15
df_shj_modcollect.loc[2030] = 45
df_shj_modcollect.loc[2050] = 75
df_shj_modcollect.interpolate(inplace=True)
#collection
sim1.modifyScenario('SHJ', 'mod_EOL_collection_eff', df_shj_modcollect.loc[2022:,'mod_collect'], start_year=2022) #
#module recycling target
df_shj_modrecycle = pd.DataFrame(index=idx_temp, columns=['mod_recycle'], dtype=float)
df_shj_modrecycle.loc[2022] = 75
df_shj_modrecycle.loc[2030] = 80
df_shj_modrecycle.loc[2050] = 95
df_shj_modrecycle.interpolate(inplace=True)
#recycling target
sim1.modifyScenario('SHJ', 'mod_EOL_pg4_recycled', df_shj_modrecycle, start_year=2022) #

#glass 
#MFGing
#sim1.scenario['SHJ'].modifyMaterials('glass', 'mat_MFG_scrap_Recycled_into_HQ', 0.0, start_year=2022) 
#EoL
#recycling target
df_shj_recycleglass_target = 100.0
sim1.scenario['SHJ'].modifyMaterials('glass', 'mat_PG4_Recycling_target', df_shj_recycleglass_target, start_year=2022)
#glass recycling yield
df_shj_recycleglass_yld = pd.DataFrame(index=idx_temp, columns=['glass_recycle_yield'], dtype=float)
df_shj_recycleglass_yld.loc[2022] = 40
df_shj_recycleglass_yld.loc[2030] = 60
df_shj_recycleglass_yld.loc[2050] = 80
df_shj_recycleglass_yld.interpolate(inplace=True)
sim1.scenario['SHJ'].modifyMaterials('glass', 'mat_Recycling_yield', df_shj_recycleglass_yld, start_year=2022)
#glass to HQ
df_shj_recycleglass_HQ = pd.DataFrame(index=idx_temp, columns=['glass_recycle_HQ'], dtype=float)
df_shj_recycleglass_HQ.loc[2022] = 0
df_shj_recycleglass_HQ.loc[2030] = 100
df_shj_recycleglass_HQ.loc[2050] = 100
df_shj_recycleglass_HQ.interpolate(inplace=True)
sim1.scenario['SHJ'].modifyMaterials('glass', 'mat_EOL_Recycled_into_HQ', df_shj_recycleglass_HQ, start_year=2022)
#glass to HQCL
df_shj_recycleglass_HQCL = pd.DataFrame(index=idx_temp, columns=['glass_recycle_HQCL'], dtype=float)
df_shj_recycleglass_HQCL.loc[2022] = 0
df_shj_recycleglass_HQCL.loc[2030] = 45
df_shj_recycleglass_HQCL.loc[2050] = 75
df_shj_recycleglass_HQCL.interpolate(inplace=True)
sim1.scenario['SHJ'].modifyMaterials('glass', 'mat_EOL_RecycledHQ_Reused4MFG', df_shj_recycleglass_HQCL, start_year=2022)

#AL frames 
#aluminium_frames recycle
#mfg scrap
sim1.scenario['SHJ'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) 
sim1.scenario['SHJ'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['SHJ'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['SHJ'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
#no remfg
sim1.scenario['SHJ'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['SHJ'].modifyMaterials('aluminium_frames', 'mat_ReMFG_yield', 0.0, start_year=2022) #0% yeild

#recycling target
df_shj_recyclealframe_target = 100.0
sim1.scenario['SHJ'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target',df_shj_recyclealframe_target, start_year=2022) #send to recycle
#Al frame yield
sim1.scenario['SHJ'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 98.0, start_year=2022) #
#al frames to HQ
df_shj_recyclealframe_HQ = 100.0
sim1.scenario['SHJ'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', df_shj_recyclealframe_HQ, start_year=2022) #all HQ
#Al frames to closed loop
df_shj_recyclealframe_HQCL = pd.DataFrame(index=idx_temp, columns=['al_frames_recycle_HQCL'], dtype=float)
df_shj_recyclealframe_HQCL.loc[2022] = 20
df_shj_recyclealframe_HQCL.loc[2030] = 60
df_shj_recyclealframe_HQCL.loc[2050] = 100
df_shj_recyclealframe_HQCL.interpolate(inplace=True)
sim1.scenario['SHJ'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', df_shj_recyclealframe_HQCL, start_year=2022) #closed-loop

#silicon 
#MFGing
#EoL
#recycling target
df_shj_recycleSi_target = pd.DataFrame(index=idx_temp, columns=['si_recycled'], dtype=float)
df_shj_recycleSi_target.loc[2022] = 20
df_shj_recycleSi_target.loc[2030] = 100
df_shj_recycleSi_target.loc[2050] = 100
df_shj_recycleSi_target.interpolate(inplace=True)
sim1.scenario['SHJ'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', df_shj_recycleSi_target, start_year=2022)
#recycling yield
sim1.scenario['SHJ'].modifyMaterials('silicon', 'mat_Recycling_yield', 90.0, start_year=2022) 
sim1.scenario['SHJ'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycling_eff', 90.0, start_year=2022) 
#silicon recycling to HQ
df_shj_recycleSi_HQ = pd.DataFrame(index=idx_temp, columns=['si_recycled_HQ'], dtype=float)
df_shj_recycleSi_HQ.loc[2022] = 0
df_shj_recycleSi_HQ.loc[2030] = 40
df_shj_recycleSi_HQ.loc[2050] = 75
df_shj_recycleSi_HQ.interpolate(inplace=True)
sim1.scenario['SHJ'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', df_shj_recycleSi_HQ, start_year=2022) 
sim1.scenario['SHJ'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ', df_shj_recycleSi_HQ, start_year=2022) 
#silicon recycling to HQCL
df_shj_recycleSi_HQCL = pd.DataFrame(index=idx_temp, columns=['si_recycled_HQCL'], dtype=float)
df_shj_recycleSi_HQCL.loc[2022] = 0
df_shj_recycleSi_HQCL.loc[2030] = 100
df_shj_recycleSi_HQCL.loc[2050] = 100
df_shj_recycleSi_HQCL.interpolate(inplace=True)
sim1.scenario['SHJ'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', df_shj_recycleSi_HQCL, start_year=2022) #closed-loop
sim1.scenario['SHJ'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', df_shj_recycleSi_HQCL, start_year=2022) #closed-loop


sim1.scenario['SHJ'].material['silicon'].matdataIn_m#.iloc[:,2:]


plt.plot(sim1.scenario['SHJ'].material['silicon'].matdataIn_m.iloc[:,0],sim1.scenario['SHJ'].material['silicon'].matdataIn_m.iloc[:,3:])
plt.legend(sim1.scenario['PV_ICE'].material['encapsulant'].matdataIn_m.columns, 
           bbox_to_anchor=(0, -0.1), loc='upper left', ncol=2)

<matplotlib.legend.Legend at 0x212ab75feb0>


#module efficiency modify for PERC
sim1.modifyScenario('TOPCon', 'mod_eff', celltech_modeff.loc[2022:,'TOPCon'], start_year=2022) #changing module eff
#sim1.scenario['SHJ'].dataIn_m.loc[timeshift:,'mod_eff'] = celltech_modeff.loc[2022:,'SHJ'].values


#modify silver usage for SHJ
sim1.scenario['TOPCon'].modifyMaterials('silver', 'mat_massperm2', celltech_aguse.loc[2022:,'TOPCon'], start_year=2022)


#modify package to glass glass
sim1.scenario['TOPCon'].modifyMaterials('glass', 'mat_massperm2', glassperm2, start_year=2022)


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('TOPCon', 'mod_degradation', 0.7, start_year=2022) #annual power degradation
#Mod Project Lifetime
sim1.modifyScenario('TOPCon', 'mod_lifetime', 30, start_year=2022) #project lifetime of 30 years
#T50
sim1.modifyScenario('TOPCon', 'mod_reliability_t50', 28, start_year=2022)
#t90
sim1.modifyScenario('TOPCon', 'mod_reliability_t90', 33, start_year=2022)


#https://www.ise.fraunhofer.de/en/press-media/press-releases/2022/solar-cells-from-recycled-silicon.html
#lets assume this gets slightly better over time
idx_RePerc_eff = pd.RangeIndex(start=2022,stop=2051,step=1) #create the index
df_RePerc_eff = pd.DataFrame(index=idx_RePerc_eff, columns=['mod_eff_RePerc'], dtype=float)
df_RePerc_eff.loc[2022] = 19.7
df_RePerc_eff.loc[2050] = 22.2
df_RePerc_eff.interpolate(inplace=True)


#module efficiency
sim1.modifyScenario('RecycledPERC', 'mod_eff', df_RePerc_eff['mod_eff_RePerc'], start_year=2022)


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('RecycledPERC', 'mod_degradation', 0.8, start_year=2022) #slightly higher than current std
#Mod Project Lifetime
sim1.modifyScenario('RecycledPERC', 'mod_lifetime', 20, start_year=2022) #project lifetime of 20 years
#T50
sim1.modifyScenario('RecycledPERC', 'mod_reliability_t50', 21, start_year=2022)
#t90
sim1.modifyScenario('RecycledPERC', 'mod_reliability_t90', 26, start_year=2022)


#silicon recycled
#mfg scrap
sim1.scenario['RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycling_eff', 98.0, start_year=2022) #98% yield
sim1.scenario['RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['RecycledPERC'].modifyMaterials('silicon', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['RecycledPERC'].modifyMaterials('silicon', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['RecycledPERC'].modifyMaterials('silicon', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['RecycledPERC'].modifyMaterials('silicon', 'mat_Recycling_yield', 98.0, start_year=2022) #99% yeild
sim1.scenario['RecycledPERC'].modifyMaterials('silicon', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['RecycledPERC'].modifyMaterials('silicon', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop

#aluminium_frames recycle
#mfg scrap
sim1.scenario['RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled', 100.0, start_year=2022) #send mfg scrap to recycle
sim1.scenario['RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycling_eff', 99.0, start_year=2022) #98% yield
sim1.scenario['RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_MFG_scrap_Recycled_into_HQ_Reused4MFG', 100.0, start_year=2022) #closed-loop
#eol
sim1.scenario['RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_PG3_ReMFG_target', 0.0, start_year=2022) #send to recycle
sim1.scenario['RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_PG4_Recycling_target', 100.0, start_year=2022) #send to recycle
sim1.scenario['RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_Recycling_yield', 99.0, start_year=2022) #99% yeild
sim1.scenario['RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_EOL_Recycled_into_HQ', 100.0, start_year=2022) #all HQ
sim1.scenario['RecycledPERC'].modifyMaterials('aluminium_frames', 'mat_EOL_RecycledHQ_Reused4MFG', 100.0, start_year=2022) #closed-loop


#the cool feature of this module was that the wafer was directly put into the Cz process
#therefore, we need to modify the recycling energy to reflect this
#this energy profile is output by the (baseline development) Energy Silicon Manufacturing journal.
altHQRecycle_e = pd.read_csv(os.path.join(supportMatfolder, 'output_energy_silicon_eol_recycleHQ_ALT.csv'), index_col=0)


altHQRecycle_e


sim1.scenario['RecycledPERC'].modifyMaterialEnergy('silicon', 'e_mat_Recycled_HQ', altHQRecycle_e.loc[2022:,'E_MFG_kWhpkg'], start_year=2022)
sim1.scenario['RecycledPERC'].modifyMaterialEnergy('silicon', 'e_mat_Recycled_HQ_fuelfraction', altHQRecycle_e.loc[2022:,'E_mfgFuelFraction'], start_year=2022)


sim1.scenario['RecycledPERC'].material['silicon'].matdataIn_e.iloc[25:30,]


#module efficiency modify for PERC
sim1.modifyScenario('CheapCrap', 'mod_eff', 19.0, start_year=2022) #changing module eff


#Lifetime and Degradation
#values taken from lifetime vs recycling paper
#degradation rate:
sim1.modifyScenario('CheapCrap', 'mod_degradation', 1.0, start_year=2022) #annual power degradation
#Mod Project Lifetime
sim1.modifyScenario('CheapCrap', 'mod_lifetime', 25, start_year=2022) #project lifetime of 25 years

#modify alpha beta instead of T50T90, according to Henry Hieslmair bin D quality values
#alpha = 5.692, beta = 29.697
#H. Hieslmair, “Assessing the ‘useful life’ of PV Modules: Reaching for 40 and 50 year module useful life,” 
#presented at the NREL PVRW, Feb. 18, 2021. Accessed: Feb. 26, 2021. [Online]. Available: https://www.youtube.com/watch?v=M7BHcxxugwY&feature=youtu.be
#do this when we run the simulation below
#Modify weibull params for cheap crap only and re run
cheapcrapweibull = {'alpha':5.692,
                   'beta':29.697}


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim1.trim_Years(startYear=2000, endYear=2100)

Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Mass, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Mass, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Mass, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Mass, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Mass, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Mass, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Energy, years now encompass  2000  to  2100
Data trimmed for Mass, years now encompass  2000  to  2100


#check
sim1.scenario['SHJ'].material['glass'].matdataIn_e


global_projection = pd.read_csv(os.path.join(supportMatfolder,'output-globalInstallsProjection.csv'), index_col=0)

fig, ax1 = plt.subplots()

ax1.stackplot(global_projection.index, global_projection['World_cum']/1e6, color='#F7A11A')
ax1.set_ylabel('Cumulative Solar Capacity [TW]', color='#F7A11A')
ax2 = ax1.twinx()
ax2.plot(global_projection['World_annual_[MWdc]']/1e6)
ax2.set_ylabel('Annual Installations [TW]')
plt.show()


#deployment projection for all scenarios
sim1.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', value= global_projection['World_annual_[MWdc]'], start_year=2000)
#for scen in scennames:
#    sim1.scenario[scen].dataIn_m.loc[0:len(global_projection.index-1),'new_Installed_Capacity_[MW]'] = global_projection['World_annual_[MWdc]'].values


sim1.calculateMassFlow()
sim1.calculateFlows(scenarios=['CheapCrap'], weibullInputParams=cheapcrapweibull)

>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames
Working on Scenario:  PERC_50
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames
Working on Scenario:  SHJ
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames
Working on Scenario:  TOPCon
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames
Working on Scenario:  Perovskite
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
ReMFG surplus End of Sim for Mat  glass  Scenario  Perovskite  =  1213468789.114702  tonnes.
Recycled surplus End of Sim for Mat  glass  Scenario  Perovskite  =  9121295.856909094  tonnes.
==> Working on Material :  silver
==> Working on Material :  silicon
Recycled surplus End of Sim for Mat  silicon  Scenario  Perovskite  =  33071992.272860732  tonnes.
==> Working on Material :  copper
==> Working on Material :  aluminium_frames
Recycled surplus End of Sim for Mat  aluminium_frames  Scenario  Perovskite  =  115382017.01697423  tonnes.
Working on Scenario:  RecycledPERC
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames
Working on Scenario:  CheapCrap
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames
>>>> Calculating Material Flows <<<<

Working on Scenario:  CheapCrap
********************
Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames


>>>> Calculating Energy Flows <<<<

Working on Scenario:  CheapCrap
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  aluminium_frames


ii_yearly, ii_cumu = sim1.aggregateResults() #have to do this to get auto plots


ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']

year
2000    8.086690e+02
2001    1.092986e+03
2002    1.437059e+03
2003    1.965030e+03
2004    3.051979e+03
            ...     
2096    6.008872e+07
2097    6.030804e+07
2098    6.052736e+07
2099    6.074668e+07
2100    6.096600e+07
Name: newInstalledCapacity_sim1_PV_ICE_[MW], Length: 101, dtype: float64


sim1.scenario['CheapCrap'].dataOut_m['WeibullParams']

0      {'alpha': 5.692, 'beta': 29.697}
1      {'alpha': 5.692, 'beta': 29.697}
2      {'alpha': 5.692, 'beta': 29.697}
3      {'alpha': 5.692, 'beta': 29.697}
4      {'alpha': 5.692, 'beta': 29.697}
                     ...               
96     {'alpha': 5.692, 'beta': 29.697}
97     {'alpha': 5.692, 'beta': 29.697}
98     {'alpha': 5.692, 'beta': 29.697}
99     {'alpha': 5.692, 'beta': 29.697}
100    {'alpha': 5.692, 'beta': 29.697}
Name: WeibullParams, Length: 101, dtype: object


effective_capacity = ii_yearly.filter(like='ActiveCapacity')
plt.plot(ii_cumu['newInstalledCapacity_sim1_PV_ICE_[MW]']/1e6, label='Capacity Target', color='black', ls='--')
plt.plot(effective_capacity/1e6, label=scennames_labels)
plt.legend()
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: No Replacements')
plt.ylim(0,)

(0.0, 64.01425956655)


bifiFactors = {'PERC_50':0.7, # ITRPV 2022, Fig. 58
               'SHJ':0.9, # ITRPV 2022, Fig. 58
               'TOPCon':0.8, # ITRPV 2022, Fig. 58
               'Perovskite': 0.0,
               'RecycledPERC':0.6,
                'PV_ICE':0.0,
                'CheapCrap':0.0} 

#MAY NEED TO CHANGE TO BE DYNAMIC


#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
idx_temp = pd.RangeIndex(start=2000,stop=2101,step=1) #create the index
df_temp = pd.DataFrame(index=idx_temp, columns=['bifi'], dtype=float)
bifiPathDict={}

for f in bifiFactors.keys(): #loop over module types
    bifi = df_temp.copy() #copy of df
    bifi['bifi'] = bifiFactors[f] #assign column
    bifipath = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi.to_csv(path_or_buf=bifipath, index_label='year') #create file
    bifiPathDict[str(f)] = bifipath
    #append bifi path to dict? or list?


#currently takes 15 mins to run with 5 mateirals and 5 scenarios

for row in range (0,len(sim1.scenario['PV_ICE'].dataIn_m)): #loop over length of years
    print(row)
    for scen in scennames: #loop over scenarios
        print(scen)
        Under_Installment = global_projection.iloc[row,0] - ((sim1.scenario[scen].dataOut_m['Installed_Capacity_[W]'][row])/1e6)  # MWATTS
        sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed
        #calculate flows for that scenario with it's bifi factor and modified weibull
        if scen == 'CheapCrap':
            sim1.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict[scen], weibullInputParams=cheapcrapweibull)
        else:
            sim1.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict[scen])

0
PV_ICE
>>>> Calculating Material Flows <<<<

Working on Scenario:  PV_ICE
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_13304\940628017.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames


>>>> Calculating Energy Flows <<<<

Working on Scenario:  PV_ICE
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  aluminium_frames
PERC_50
>>>> Calculating Material Flows <<<<

Working on Scenario:  PERC_50
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_13304\940628017.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames


>>>> Calculating Energy Flows <<<<

Working on Scenario:  PERC_50
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  aluminium_frames
SHJ
>>>> Calculating Material Flows <<<<

Working on Scenario:  SHJ
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_13304\940628017.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames


>>>> Calculating Energy Flows <<<<

Working on Scenario:  SHJ
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  aluminium_frames
TOPCon
>>>> Calculating Material Flows <<<<

Working on Scenario:  TOPCon
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_13304\940628017.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed

Finished Area+Power Generation Calculations
==> Working on Material :  glass
==> Working on Material :  silver
==> Working on Material :  silicon
==> Working on Material :  copper
==> Working on Material :  aluminium_frames


>>>> Calculating Energy Flows <<<<

Working on Scenario:  TOPCon
********************
==> Working on Energy for Material :  glass
==> Working on Energy for Material :  silver
==> Working on Energy for Material :  silicon
==> Working on Energy for Material :  copper
==> Working on Energy for Material :  aluminium_frames
Perovskite
>>>> Calculating Material Flows <<<<

Working on Scenario:  Perovskite
********************

C:\Users\hmirletz\AppData\Local\Temp\1\ipykernel_13304\940628017.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  sim1.scenario[scen].dataIn_m['new_Installed_Capacity_[MW]'][row] += Under_Installment #overwrite new installed


sim1.scenario['CheapCrap'].dataOut_m['WeibullParams'].tail(2)

99     {'alpha': 5.692, 'beta': 29.697}
100    {'alpha': 5.692, 'beta': 29.697}
Name: WeibullParams, dtype: object


plt.plot(sim1.scenario['CheapCrap'].dataOut_m['Installed_Capacity_[W]'])

[<matplotlib.lines.Line2D at 0x22f3ff593d0>]


#sim1.scenario['PV_ICE'].dataOut_m['irradiance_stc'].head(2)
sim1.scenario['PV_ICE'].dataOut_m['WeibullParams']

0      {'alpha': 6.283760582394805, 'beta': 20.141170...
1      {'alpha': 6.283760582394805, 'beta': 20.141170...
2      {'alpha': 6.283760582394805, 'beta': 20.141170...
3      {'alpha': 6.283760582394805, 'beta': 20.141170...
4      {'alpha': 6.283760582394805, 'beta': 20.141170...
                             ...                        
96     {'alpha': 12.596192434998898, 'beta': 41.18098...
97     {'alpha': 12.596192434998898, 'beta': 41.18098...
98     {'alpha': 12.596192434998898, 'beta': 41.18098...
99     {'alpha': 12.596192434998898, 'beta': 41.18098...
100    {'alpha': 12.596192434998898, 'beta': 41.18098...
Name: WeibullParams, Length: 101, dtype: object


sim1.scenario['PERC_50'].dataOut_m['irradiance_stc'].head(2)

0    1070.0
1    1070.0
Name: irradiance_stc, dtype: float64


sim1.scenario['Perovskite'].dataOut_m['irradiance_stc'].head(2)

0    1000.0
1    1000.0
Name: irradiance_stc, dtype: float64


sim1.scenario['RecycledPERC'].dataOut_m['irradiance_stc'].head(2)

0    1060.0
1    1060.0
Name: irradiance_stc, dtype: float64


#aggregate results - mass
cc_yearly, cc_cumu = sim1.aggregateResults() #have to do this to get auto plots
cc_yearly.to_csv(os.path.join(testfolder, 'cc_yearly.csv'))
cc_cumu.to_csv(os.path.join(testfolder, 'cc_cumu.csv'))


#aggregate results - energy
allenergy, energyGen, energy_demands = sim1.aggregateEnergyResults()
allenergy.to_csv(os.path.join(testfolder, 'cc_allenergy.csv'))
energyGen.to_csv(os.path.join(testfolder, 'cc_energyGen.csv'))
energy_demands.to_csv(os.path.join(testfolder, 'cc_energy_demands.csv'))

C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:1868: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:1868: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:1868: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:1868: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:1868: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)
C:\Users\hmirletz\Documents\GitHub\PV_ICE\PV_ICE\main.py:1868: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  energy_demands[colname] = energy_demands.filter(like=scen).sum(axis=1)


#read in saved results files for speed
cc_yearly = pd.read_csv(os.path.join(testfolder, 'cc_yearly.csv'), index_col='year')
cc_cumu = pd.read_csv(os.path.join(testfolder, 'cc_cumu.csv'), index_col='year')
allenergy = pd.read_csv(os.path.join(testfolder, 'cc_allenergy.csv'), index_col='year')
energyGen = pd.read_csv(os.path.join(testfolder, 'cc_energyGen.csv'), index_col='year')
energy_demands = pd.read_csv(os.path.join(testfolder, 'cc_energy_demands.csv'), index_col='year')


sim1.plotMetricResults()

[]


effective_capacity = cc_yearly.filter(like='ActiveCapacity')
plt.plot(effective_capacity/1e6)
plt.legend(scennames_labels)
plt.ylabel('Effective Capacity [TW]')
plt.title('Effective Capacity: With Replacements')
plt.ylim(0,)

(0.0, 64.01425956655)


annual_EoL = cc_yearly.filter(like='DecommisionedCapacity')
plt.plot(annual_EoL/1e6)
plt.legend(scennames_labels)
plt.ylabel('Annual EoL [TW]')
plt.title('Annual Decommissions [TW]')
plt.ylim(0,)

(0.0, 264.9415075728642)


annual_installs = cc_yearly.filter(like='newInstalled')
plt.plot(annual_installs/1e6)
plt.legend(scennames_labels)
plt.ylabel('Annual installed [TW]')
plt.title('Annual Installs with Replacements')
plt.ylim(0,)

(0.0, 6.238887660174118)


cumu_installs_annually = cc_cumu.filter(like='newInstalled')
plt.plot(cumu_installs_annually/1e6)
plt.legend(scennames_labels)
plt.ylabel('Cumulative installed [TW]')
plt.title('Replacements Adjusted Deployment Curve \n Cumulative Installs with Replacements')
plt.ylim(0,)

(0.0, 328.9557671394142)


cumu_installs = cc_cumu.filter(like='newInstalled')
plt.bar(scennames, cumu_installs.loc[2100]/1e6, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.ylabel('Cumulative installed [TW]')
plt.title('Cumulative Installs with Replacements')

Text(0.5, 1.0, 'Cumulative Installs with Replacements')


cumulative_nameplate_installs = global_projection.loc[2100,'World_cum'] #MW
print('The nameplate installations for energy transition and through 2100 are '+str(cumulative_nameplate_installs/1e6)+' TW.')

The nameplate installations for energy transition and through 2100 are 60.966 TW.


global_projection['World_annual_[MWdc]'].sum()

60966000.00000001


Additional_installs = cumu_installs.loc[2100]-global_projection.loc[2100,'World_cum']
plt.bar(scennames, Additional_installs/1e6, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.ylabel('Cumulative Replacements [TW]')
plt.title('Replacements Required by Technology')

Text(0.5, 1.0, 'Replacements Required by Technology')


cumu_virgin_module = cc_cumu.filter(like='VirginStock_Module')
plt.bar(scennames, cumu_virgin_module.loc[2100]/1e9, tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.title('Cumulative Virgin Material Demands')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')

Text(0, 0.5, 'Virgin Material Requirements\n[billion tonnes]')


recycledperc_virginstock = cc_cumu.filter(like='VirginStock').filter(like='Recycled')
recycledperc_virginstock.drop('VirginStock_Module_sim1_RecycledPERC_[Tonnes]',axis=1, inplace=True)
plt.bar(recycledperc_virginstock.columns, recycledperc_virginstock.loc[2100], tick_label = MATERIALS, color=colorpalette)

<BarContainer object of 5 artists>


cumu_virgin_module = cc_cumu.filter(like='VirginStock_Module')
plt.plot(cumu_virgin_module/1e9)
plt.legend(scennames_labels)
plt.title('Cumulative Virgin Material Extraction: Module')
plt.ylabel('Virgin Material Requirements\n[billion tonnes]')
plt.ylim(0,)

(0.0, 12.700168282372587)


annual_virgin_module = cc_yearly.filter(like='VirginStock_Module')
plt.plot(annual_virgin_module/1e6)
plt.legend(scennames_labels)
plt.title('Annual Virgin Material Extraction: Module')
plt.ylabel('Virgin Material Requirements\n[million tonnes]')
plt.ylim(0,)

(0.0, 280.0945453135688)


#print out masses for stacked bar charts
#mass_cumu = cc_cumu.loc[[2100]].filter(like='PV_ICE_[Tonnes]')
mass_cumu_virgin = cc_cumu.filter(like='VirginStock').loc[2100]
mass_cumu_wasteall = cc_cumu.filter(like='WasteAll').loc[2100]
mass_cumu = pd.concat([mass_cumu_virgin,mass_cumu_wasteall])
mass_cumu.to_csv(os.path.join(testfolder, 'cc_cumu_mass_2100results.csv'))


cc_cumu.to_csv(os.path.join(testfolder, 'cc_cumu_mass_results.csv'))


cumu_lifecycle_wastes = cc_cumu.filter(like='WasteAll_Module')

plt.bar(scennames, cumu_lifecycle_wastes.loc[2100]/1e6, 
        tick_label=scennames_labels, color=colorpalette)
#plt.legend(scennames)
plt.title('Cumulative Lifecycle Wastes')
plt.ylabel('Lifecycle Wastes\n[million tonnes]')

Text(0, 0.5, 'Lifecycle Wastes\n[million tonnes]')


sim1.scenario['SHJ'].dataOut_e


#energyGen = allenergy.filter(like='e_out_annual')
plt.plot(energyGen/1e12)
plt.legend(scennames_labels)
plt.title('Annual Energy Generation')
plt.ylabel('Energy Generation [TWh]')
plt.ylim(0,)

(0.0, 103909.74394690627)


e_annual_sumDemands = energy_demands.filter(like='demand_total')


plt.plot(e_annual_sumDemands/1e12)
plt.legend(scennames_labels)
plt.title('Annual Lifecycle Energy Demands')
plt.ylabel('Energy Demands\n[TWh]')
plt.ylim(0,)

(0.0, 4714.805154070371)


e_annual_sumDemands_cumu = e_annual_sumDemands.cumsum()


plt.bar(e_annual_sumDemands_cumu.columns, e_annual_sumDemands_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Cumulative Lifecycle Energy Demands')
plt.ylabel('Cumulative Energy Demands\n[TWh]')

Text(0, 0.5, 'Cumulative Energy Demands\n[TWh]')


energyGen_cumu = energyGen.cumsum()
energyGen_cumu.columns = e_annual_sumDemands_cumu.columns = scennames
netEnergy_cumu = energyGen_cumu.loc[[2100]] - e_annual_sumDemands_cumu.loc[[2100]]


e_annual_sumDemands_cumu.loc[[2100]]


netEnergy_cumu


plt.bar(netEnergy_cumu.columns, netEnergy_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy [TWh]')
plt.ylim(4e6,5.5e6)

(4000000.0, 5500000.0)


netEnergy_relative = netEnergy_cumu - netEnergy_cumu.loc[2100,'PV_ICE']
netEnergy_relative
plt.bar(netEnergy_relative.columns, netEnergy_relative.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Cumulatively Net Energy Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy [TWh]')

Text(0, 0.5, 'Relative Cumulative Net Energy [TWh]')


netEnergy_cumu_norm = netEnergy_cumu/netEnergy_cumu.loc[2100,'PV_ICE']
netEnergy_cumu_norm_waterfall = netEnergy_cumu_norm-1
netEnergy_cumu_norm


plt.bar(netEnergy_cumu.columns, netEnergy_cumu_norm_waterfall.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively Relative to PV ICE')
plt.ylabel('Relative Cumulative Net Energy')
#plt.ylim(-0.026,0.005)
plt.plot(0.0, lw=2)

[<matplotlib.lines.Line2D at 0x22f46cd1f70>]


plt.bar(netEnergy_cumu.columns, netEnergy_cumu.loc[2100]/1e12, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Net Energy Cumulatively')
plt.ylabel('Cumulative Net Energy\n[TWh]')

Text(0, 0.5, 'Cumulative Net Energy\n[TWh]')


energyGen.columns = e_annual_sumDemands.columns = scennames
annual_net_energy = energyGen - e_annual_sumDemands


plt.plot(annual_net_energy/1e12)
plt.legend(scennames_labels)
plt.title('Annual Net Energy')
plt.ylabel('Net Energy (Eout - Ein)\n[TWh]')
plt.ylim(0,100000)
plt.xlim(2000,2100)

(2000.0, 2100.0)


eroi_sys_cumu = energyGen_cumu/e_annual_sumDemands_cumu


plt.bar(eroi_sys_cumu.columns, eroi_sys_cumu.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('EROI of all Deployments')
plt.ylabel('Unitless')

Text(0, 0.5, 'Arbitrary units')


#cumulatively
netEnergy_cumu.columns = cumu_virgin_module.columns = cumu_lifecycle_wastes.columns = scennames_labels

CEETmetric = (netEnergy_cumu.loc[2100]/1e6)/ (cumu_virgin_module.loc[2100]+ cumu_lifecycle_wastes.loc[2100])


plt.bar(CEETmetric.index, CEETmetric, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('CETE Metric')
plt.ylabel('Net Energy / Linear Mass \n[MWh/tonne]')


single_deploy_2022 = pd.DataFrame(index=idx_temp, columns=['MW'], dtype=float)
single_deploy_2022['MW'] = 0.0
single_deploy_2022.loc[2022] = 100.0 #install a single 100 MW system


sim2 = PV_ICE.Simulation(name='sim2', path=testfolder)
for scen in scennames:
    sim2.createScenario(name=scen, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim2.scenario[scen].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#use the settings from the first simulation to create the identical settings for sim2
for scen in scennames:
    sim2.scenario[scen].dataIn_m = sim1.scenario[scen].dataIn_m
    sim2.scenario[scen].dataIn_e = sim1.scenario[scen].dataIn_e
    for mat in MATERIALS:
        sim2.scenario[scen].material[mat].matdataIn_m = sim1.scenario[scen].material[mat].matdataIn_m
        sim2.scenario[scen].material[mat].matdataIn_e = sim1.scenario[scen].material[mat].matdataIn_e


sim2.scenario['Perovskite'].material['glass'].matdataIn_m


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim2.trim_Years(startYear=2000, endYear=2100)


#deployment projection for all scenarios
sim2.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', value= single_deploy_2022['MW'], start_year=2000)


sim2.scenario['Perovskite'].dataIn_m.loc[19:25]


for scen in scennames:
    sim2.calculateFlows(scenarios=[scen],bifacialityfactors=bifiPathDict[scen])


sim2_yearly, sim2_cumulative = sim2.aggregateResults()


allenergy2, energyGen2, energy_demands2 = sim2.aggregateEnergyResults()


allenergy2_cumu = allenergy2.cumsum()
energyGen2_cumu = energyGen2.cumsum()
energy_demands2_cumu = energy_demands2.cumsum()


energy_demands2_cumu_total = energy_demands2_cumu.filter(like='demand_total')
#energy_demands2_cumu_total


plt.bar(energy_demands2_cumu_total.columns, energy_demands2_cumu_total.loc[2100], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Energy Demands Cumulative')
plt.ylabel('Energy Demands [Wh]')


energyGen2_cumu.columns=energy_demands2_cumu_total.columns=scennames_labels
eroi_2022 = energyGen2_cumu.iloc[100]/energy_demands2_cumu_total.iloc[100]
eroi_2022


plt.bar(eroi_2022.index, eroi_2022, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('EROI')
plt.ylabel('Arb.')


plt.plot(energyGen2/1e9)
plt.legend(scennames_labels)
plt.title('Energy Generation - Single 2022 System')
plt.ylabel('[GWh]')


single_deploy_2050 = pd.DataFrame(index=idx_temp, columns=['MW'], dtype=float)
single_deploy_2050['MW'] = 0.0
single_deploy_2050.loc[2050] = 100.0 #install a single 100 MW system


sim3 = PV_ICE.Simulation(name='sim3', path=testfolder)
for scen in scennames:
    sim3.createScenario(name=scen, massmodulefile=moduleFile_m, energymodulefile=moduleFile_e)
    for mat in range (0, len(MATERIALS)):
        matbaseline_m = os.path.join(baselinesfolder,'baseline_material_mass_'+MATERIALS[mat]+'.csv')
        matbaseline_e = os.path.join(baselinesfolder,'baseline_material_energy_'+MATERIALS[mat]+'.csv')
        sim3.scenario[scen].addMaterial(MATERIALS[mat], massmatfile=matbaseline_m, energymatfile=matbaseline_e)


#use the settings from the first simulation to create the identical settings for sim2
for scen in scennames:
    sim3.scenario[scen].dataIn_m = sim1.scenario[scen].dataIn_m
    sim3.scenario[scen].dataIn_e = sim1.scenario[scen].dataIn_e
    for mat in MATERIALS:
        sim3.scenario[scen].material[mat].matdataIn_m = sim1.scenario[scen].material[mat].matdataIn_m
        sim3.scenario[scen].material[mat].matdataIn_e = sim1.scenario[scen].material[mat].matdataIn_e


sim3.scenario['Perovskite'].material['glass'].matdataIn_m


#deployment projection for all scenarios
sim3.modifyScenario(scenarios=None,stage='new_Installed_Capacity_[MW]', value= single_deploy_2050['MW'], start_year=2000)


#trim to start in 2000, this trims module and materials
#had to specify and end year, cannot use to extend
sim3.trim_Years(startYear=2000, endYear=2150)


sim3.scenario['Perovskite'].dataIn_e.tail(4)#loc[49:55]


#PV ICE currently set up to read in a csv of bifi factors, so generate files to read in 
#need to adjust for length to 2150
idx_temp_2150 = pd.RangeIndex(start=2000,stop=2151,step=1) #create the index
df_temp_2150 = pd.DataFrame(index=idx_temp_2150, columns=['bifi'], dtype=float)
bifiPathDict_2150={}

for f in bifiFactors.keys(): #loop over module types
    bifi_2150 = df_temp_2150.copy() #copy of df
    bifi_2150['bifi'] = bifiFactors[f] #assign column
    bifipath_2150 = os.path.join(testfolder,'bifi_'+str(f)+'.csv') #create file path
    bifi_2150.to_csv(path_or_buf=bifipath_2150, index_label='year') #create file
    bifiPathDict_2150[str(f)] = bifipath_2150
    #append bifi path to dict? or list?


for scen in scennames:
    sim3.calculateFlows(scenarios=[scen], bifacialityfactors=bifiPathDict_2150[scen])


sim3_yearly, sim3_cumulative = sim3.aggregateResults()


allenergy3, energyGen3, energy_demands3 = sim3.aggregateEnergyResults()


allenergy3_cumu = allenergy3.cumsum()
energyGen3_cumu = energyGen3.cumsum()
energy_demands3_cumu = energy_demands3.cumsum()


energy_demands3_cumu_total = energy_demands3_cumu.filter(like='demand_total')
#energy_demands3_cumu_total


plt.bar(energy_demands3_cumu_total.columns, energy_demands3_cumu_total.loc[2150], 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('Energy Demands Cumulative 2050 System')
plt.ylabel('Energy Demands [Wh]')


energyGen3_cumu.columns = energy_demands3_cumu_total.columns = scennames_labels
eroi_2050 = energyGen3_cumu.loc[2150]/energy_demands3_cumu_total.loc[2150]
eroi_2050


plt.bar(eroi_2050.index, eroi_2050, 
        tick_label=(scennames_labels), color=colorpalette)
plt.title('EROI of a 100 MW deployment in 2050')
plt.ylabel('Arbitrary units')


plt.plot(energyGen3/1e9)
plt.legend(scennames_labels)
plt.title('Energy Generation - Single 2050 System')
plt.ylabel('[GWh]')

	E_MFG_kWhpkg	E_mfgFuelFraction
1995	600.817735	0.064489
1996	527.042434	0.072326
1997	485.167804	0.077357
1998	432.693843	0.085473
1999	380.620553	0.095834
2000	269.697933	0.075408
2001	236.759317	0.066235
2002	204.221370	0.054188
2003	172.084094	0.037719
2004	159.669155	0.030097
2005	140.254886	0.022532
2006	130.586287	0.022463
2007	121.318358	0.022639
2008	112.451100	0.023120
2009	103.984511	0.023976
2010	95.918593	0.025298
2011	88.253345	0.027195
2012	88.388767	0.027307
2013	88.924859	0.027745
2014	89.861622	0.028498
2015	91.199054	0.029547
2016	76.337157	0.037576
2017	61.875930	0.049814
2018	62.915373	0.053028
2019	64.355487	0.056410
2020	74.096270	0.053503
2021	77.837724	0.055738
2022	77.837724	0.055738
2023	77.837724	0.055738
2024	77.837724	0.055738
2025	77.837724	0.055738
2026	77.837724	0.055738
2027	77.837724	0.055738
2028	77.837724	0.055738
2029	77.837724	0.055738
2030	77.837724	0.055738
2031	77.837724	0.055738
2032	77.837724	0.055738
2033	77.837724	0.055738
2034	77.837724	0.055738
2035	77.837724	0.055738
2036	77.837724	0.055738
2037	77.837724	0.055738
2038	77.837724	0.055738
2039	77.837724	0.055738
2040	77.837724	0.055738
2041	77.837724	0.055738
2042	77.837724	0.055738
2043	77.837724	0.055738
2044	77.837724	0.055738
2045	77.837724	0.055738
2046	77.837724	0.055738
2047	77.837724	0.055738
2048	77.837724	0.055738
2049	77.837724	0.055738
2050	77.837724	0.055738

	year	e_mat_extraction	e_mat_MFG	e_mat_MFG_fuelfraction	e_mat_MFGScrap_LQ	e_mat_MFGScrap_HQ	e_mat_Landfill	e_mat_EoL_ReMFG_clean	e_mat_Recycled_LQ	e_mat_Recycled_HQ	e_mat_Recycled_HQ_fuelfraction
25	2020	0.0062	169.06	0.22	24.5	600.0	0.09	0.4	1.6	138.380000	0.200000
26	2021	0.0062	170.73	0.21	24.5	600.0	0.09	0.4	1.6	143.040000	0.190000
27	2022	0.0062	169.05	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738
28	2023	0.0062	168.80	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738
29	2024	0.0062	169.09	0.20	24.5	600.0	0.09	0.4	1.6	77.837724	0.055738

	year	e_mat_extraction	e_mat_MFG	e_mat_MFG_fuelfraction	e_mat_MFGScrap_LQ	e_mat_MFGScrap_HQ	e_mat_Landfill	e_mat_Landfill_fuelfraction	e_mat_EoL_ReMFG_clean	e_mat_Recycled_LQ	...	e_mat_Recycled_HQ_fuelfraction	e_mod_MFG	e_mod_Install	e_mod_OandM	e_mod_Repair	e_mod_Demount	e_mod_Resell_Certify	e_mod_Store	e_mod_ReMFG_Disassembly	e_mod_Recycle_Crush
0	2000	0.0062	4.434640	80.005988	1.4	1.4	0.09	78.0	0.61	1.4	...	50.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	2001	0.0062	4.382279	79.998397	1.4	1.4	0.09	78.0	0.61	1.4	...	50.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	2002	0.0062	4.318724	80.200079	1.4	1.4	0.09	78.0	0.61	1.4	...	50.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	2003	0.0062	4.255168	80.409933	1.4	1.4	0.09	78.0	0.61	1.4	...	50.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	2004	0.0062	4.191613	80.628330	1.4	1.4	0.09	78.0	0.61	1.4	...	50.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
96	2096	0.0062	3.512724	86.798744	1.4	1.4	0.09	78.0	0.61	1.4	...	50.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
97	2097	0.0062	3.512724	86.798744	1.4	1.4	0.09	78.0	0.61	1.4	...	50.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
98	2098	0.0062	3.512724	86.798744	1.4	1.4	0.09	78.0	0.61	1.4	...	50.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
99	2099	0.0062	3.512724	86.798744	1.4	1.4	0.09	78.0	0.61	1.4	...	50.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
100	2100	0.0062	3.512724	86.798744	1.4	1.4	0.09	78.0	0.61	1.4	...	50.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Energy Analysis¶

Module Types Creation¶

Extreme modules¶

PERC_50¶

High Efficiency¶

Perovskite¶

Lightweighting¶

Evolving Realistic Modules¶

SHJ¶

TOPCon¶

Recycled PERC¶

Cheap Crap¶

Repowered¶

Modify Years 2000 to 2100¶

Apply deployment curve¶

Calculate Mass flow¶

Installation Compensation¶

Bifacial Factors¶

RESULTS: Effective Capacity and Replacements¶

RESULTS: Virgin Material Demands¶

RESULTS: Lifecycle Wastes¶

RESULTS: Energy¶

Energy Generation¶

Net Energy¶

System Level EROI¶

CE-TE Metric¶

EROI¶

EROI 2022¶

EROI of 2050 module¶

To Do¶

	year	mat_virgin_eff	mat_massperm2	mat_MFG_eff	mat_MFG_scrap_Recycled	mat_MFG_scrap_Recycling_eff	mat_MFG_scrap_Recycled_into_HQ	mat_MFG_scrap_Recycled_into_HQ_Reused4MFG	mat_PG4_Recycling_target	mat_Recycling_yield	mat_EOL_Recycled_into_HQ	mat_EOL_RecycledHQ_Reused4MFG
0	1995	20.0	848.421429	45.000000	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
1	1996	20.4	831.785714	45.000000	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
2	1997	20.8	815.150000	45.000000	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
3	1998	21.2	798.514286	45.000000	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
4	1999	21.6	781.878571	45.000000	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
5	2000	22.0	765.242857	45.000000	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
6	2001	22.4	748.607143	45.000000	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
7	2002	22.8	731.971429	45.000000	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
8	2003	23.2	715.335714	45.000000	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
9	2004	23.6	698.700000	49.365658	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
10	2005	24.0	582.250000	41.588057	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
11	2006	24.4	465.800000	33.638416	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
12	2007	24.8	442.510000	32.313886	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
13	2008	25.2	419.220000	30.959395	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
14	2009	25.6	419.220000	31.313556	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
15	2010	26.0	419.220000	49.335362	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
16	2011	26.4	419.220000	49.661818	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
17	2012	26.8	419.220000	48.637112	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
18	2013	27.2	419.220000	49.920501	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
19	2014	27.6	419.220000	50.133176	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
20	2015	28.0	419.220000	50.540356	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
21	2016	28.4	419.220000	53.417000	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
22	2017	28.8	419.220000	59.323337	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
23	2018	29.2	410.855008	63.600553	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
24	2019	29.6	402.140667	65.063940	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
25	2020	30.0	395.541833	66.726760	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
26	2021	30.0	388.943000	66.180845	100.0	20.0	0.0	100.0	20.0	80.0	0.0	0.0
27	2022	30.0	384.285000	65.587673	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
28	2023	30.0	374.969000	66.983716	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
29	2024	30.0	365.653000	68.784940	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
30	2025	30.0	359.830500	69.379740	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
31	2026	30.0	354.008000	69.848361	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
32	2027	30.0	348.573667	70.190565	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
33	2028	30.0	343.139333	70.655697	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
34	2029	30.0	337.705000	71.125574	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
35	2030	30.0	335.376000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
36	2031	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
37	2032	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
38	2033	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
39	2034	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
40	2035	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
41	2036	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
42	2037	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
43	2038	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
44	2039	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
45	2040	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
46	2041	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
47	2042	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
48	2043	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
49	2044	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
50	2045	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
51	2046	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
52	2047	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
53	2048	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
54	2049	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN
55	2050	30.0	333.047000	71.600237	100.0	90.0	NaN	NaN	NaN	90.0	NaN	NaN

	mod_MFG	mod_Install	mod_OandM	mod_Repair	mod_Demount	mod_Store	mod_Resell_Certify	mod_ReMFG_Disassembly	mod_Recycle_Crush	e_out_annual_[Wh]
0	5.133424e+10	5.251887e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	0.000000e+00	1.312654e+12
1	1.838934e+10	1.860921e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	1.419913e+00	1.774165e+12
2	2.235026e+10	2.237429e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	1.097105e+02	2.332674e+12
3	3.424444e+10	3.391656e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	1.342435e+03	3.189692e+12
4	7.015314e+10	6.875008e+07	0.0	0.0	0.000000e+00	0.0	0.0	0.0	7.714372e+03	4.954058e+12
...	...	...	...	...	...	...	...	...	...	...
96	3.284103e+13	9.209337e+10	0.0	0.0	1.261296e+10	0.0	0.0	0.0	3.598335e+11	9.753769e+16
97	3.353352e+13	9.403527e+10	0.0	0.0	1.314404e+10	0.0	0.0	0.0	3.692423e+11	9.789370e+16
98	3.411083e+13	9.565416e+10	0.0	0.0	1.363658e+10	0.0	0.0	0.0	3.770960e+11	9.824971e+16
99	3.456264e+13	9.692114e+10	0.0	0.0	1.410906e+10	0.0	0.0	0.0	3.832524e+11	9.860571e+16
100	3.486657e+13	9.777344e+10	0.0	0.0	1.454338e+10	0.0	0.0	0.0	3.873996e+11	9.896172e+16

	PV_ICE	PERC_50	SHJ	TOPCon	Perovskite	RecycledPERC	CheapCrap
year
2100	9.013140e+16	6.878861e+16	1.036094e+17	1.088721e+17	1.774339e+17	2.147130e+17	1.671781e+17

	PV_ICE	PERC_50	SHJ	TOPCon	Perovskite	RecycledPERC	CheapCrap
year
2100	4.873377e+18	5.242166e+18	5.306615e+18	5.251717e+18	4.786075e+18	5.046606e+18	4.796331e+18